Uso de Técnicas de Análise de Sentimentos em Tweets relacionados ao Meio-Ambiente
Resumo
A proliferação dos meios de comunicação na Web e a necessidade das empresas compreenderem os impactos de suas ações socioambientais junto à população afetada, torna necessária a adoção de um mecanismo automático para analisar as opiniões da população. Esta necessidade evidencia-se ainda mais no caso de empresas que tratam diretamente com alto risco ambiental. Neste contexto, o objetivo deste trabalho é a aplicação de técnicas de análise de sentimentos em tweets relacionados à temática ambiental para auxiliar empresas do setor energético na análise dos impactos das ações empreendidas ao longo do tempo através das opiniões contidas nas mídias sociais.
Referências
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Fang, Y., Si, L., Somasundaram, N., Yu, Z.: Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of the fifth ACM international conference on Web search and data mining - WSDM’12. p. 63. ACM Press, New York, USA (2012).
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Hu, M., and Liu, B. Mining and summarizing customer reviews. In KDD ’04 (New York, New York, USA, 2004), ACM, ACM Press, p. 168.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on WWW ’10. p. 591. ACM Press, New York (2010).
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Nascimento, P., Aguas, R., Lima, D.D., Kong, X., Osiek, B.: Análise de sentimento de tweets com foco em notícias. In I Brazilian Workshop on Social Network Analysis and Mining, 2012.
NICOLELLA, G.; MARQUES, J. F.; SKORUPA, L. A.; Sistema de Gestão Ambiental: aspectos teóricos e análises de um conjunto de empresas da região de Campinas, SP. Embrapa, 2004.
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., and Smeaton, A. F. Topic-dependent sentiment analysis of financial blogs. In Proceeding of the TSA ’09 (New York, USA, 2009), ACM Press, p. 9.
Pak, A., Paroubek, P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Proceedings of the Seventh conference on International Language Resources and Evaluation LREC’10 (ELRA), 1320–1326 (2010).
Pang, B., and Lee, L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 2 (2008), 1–135.
Read, J.: Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification (June), 43–48 (2005).
Sarmento, L., Carvalho, P., Silva, M.J., de Oliveira, E.: Automatic creation of a reference corpus for political opinion mining in user-generated content. In: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion - TSA ’09. p. 29. ACM Press, New York (2009).
Sharma, A., and Dey, S. A comparative study of feature selection and machine learning techniques for sentiment analysis. In RACS ’12 (New York, USA), ACM Press, p. 1.
Tumitan, D., Becker, K.: Tracking Sentiment Evolution on User-Generated Content : A Case Study on the Brazilian Political Scene. SBBD 2013 pp. 1–6 (2013).
Alves, A. L. F., Baptista, C. S., Firmino, A. A., Oliveira, M. G., Figueirêdo,H. F.. Temporal Analysis of Sentiment in Tweets: A Case Study with FIFA Confederations Cup in Brazil. DEXA (1) 2014: 81-88.
Bjørkelund, E., Burnett, T. H., and NørvK. A study of opinion mining and visualization of hotel reviews. In Proceedings of the 14th International Conference on IIWAS (New York, USA, 2012), ACM Press, p. 229.
Chaves, M., De Freitas, L., Souza, M., Vieira, R.: PIRPO: An Algorithm to Deal with Polarity in Portuguese Online Reviews from the Accommodation Sector. Natural Language Processing and Information Systems 7337, 1–5 (2012).
Eirinaki, M., Pisal, S., and Singh, J. Feature-based opinion mining and ranking. Journal of Computer and System Sciences 78, 4 (July 2012), 1175–1184.
Fang, Y., Si, L., Somasundaram, N., Yu, Z.: Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of the fifth ACM international conference on Web search and data mining - WSDM’12. p. 63. ACM Press, New York, USA (2012).
Feldman, R.: Techniques and applications for sentiment analysis. Communications of the ACM 56(4), 82 (Apr 2013).
Hu, M., and Liu, B. Mining and summarizing customer reviews. In KDD ’04 (New York, New York, USA, 2004), ACM, ACM Press, p. 168.
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on WWW ’10. p. 591. ACM Press, New York (2010).
Li, Y.M., Li, T.Y.: Deriving Marketing Intelligence over Microblogs. In: 2011 44th
Hawaii International Conference on System Sciences. pp. 1–10. IEEE (Jan 2011) Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (May 2012).
Nascimento, P., Aguas, R., Lima, D.D., Kong, X., Osiek, B.: Análise de sentimento de tweets com foco em notícias. In I Brazilian Workshop on Social Network Analysis and Mining, 2012.
NICOLELLA, G.; MARQUES, J. F.; SKORUPA, L. A.; Sistema de Gestão Ambiental: aspectos teóricos e análises de um conjunto de empresas da região de Campinas, SP. Embrapa, 2004.
O’Hare, N., Davy, M., Bermingham, A., Ferguson, P., Sheridan, P., Gurrin, C., and Smeaton, A. F. Topic-dependent sentiment analysis of financial blogs. In Proceeding of the TSA ’09 (New York, USA, 2009), ACM Press, p. 9.
Pak, A., Paroubek, P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Proceedings of the Seventh conference on International Language Resources and Evaluation LREC’10 (ELRA), 1320–1326 (2010).
Pang, B., and Lee, L. Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2, 2 (2008), 1–135.
Read, J.: Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification (June), 43–48 (2005).
Sarmento, L., Carvalho, P., Silva, M.J., de Oliveira, E.: Automatic creation of a reference corpus for political opinion mining in user-generated content. In: Proceeding of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion - TSA ’09. p. 29. ACM Press, New York (2009).
Sharma, A., and Dey, S. A comparative study of feature selection and machine learning techniques for sentiment analysis. In RACS ’12 (New York, USA), ACM Press, p. 1.
Tumitan, D., Becker, K.: Tracking Sentiment Evolution on User-Generated Content : A Case Study on the Brazilian Political Scene. SBBD 2013 pp. 1–6 (2013).
Publicado
20/07/2015
Como Citar
ALVES, André; BAPTISTA, Cláudio; DE ANDRADE, Luiz Henrique; PAES, Roberta.
Uso de Técnicas de Análise de Sentimentos em Tweets relacionados ao Meio-Ambiente. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 6. , 2015, Recife.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 37-46.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2015.10188.
